Intrusion Detection System Using Grey Theory Combined with Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 864-867
Author(s):  
Liang Sun
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feedforward Neural Network ◽  
Rapid Expansion ◽  
Grey Model ◽  
Grey Theory ◽  
Modern Computer ◽  
Detection Technology

In past few years, with the rapid expansion of computer networks, security has become an important issue for modern computer systems. Intrusion detection technology is an effective way to detect illegitimate use to ensure network security. In this paper, we proposes an intrusion detection system based on a feedforward neural network-aided grey model (FNNAGPM), by integrating a first-order single variable grey model (GM(1,1)) and a feedforward neural network. There are three phases in the system process, including initialization phase, GM(1,1) prediction phase and FNNAGPM prediction phase.

scholarly journals Intrusion Detection System for NSL-KDD Dataset Based on Deep Learning and Recursive Feature Elimination

2021 ◽  
Vol 39 (7) ◽  
pp. 1069-1079
Author(s):  
Bilal Mohammed ◽  
Ekhlas K. Gbashi
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Binary Classification ◽  
Recursive Feature Elimination ◽  
Suggested Model ◽  
Accuracy Rate ◽  
Detection Systems ◽  
Detection Technology

Intrusion detection system is responsible for monitoring the systems and detect attacks, whether on (host or on a network) and identifying attacks that could come to the system and cause damage to them, that’s mean an IDS prevents unauthorized access to systems by giving an alert to the administrator before causing any serious harm. As a reasonable supplement of the firewall, intrusion detection technology can assist systems to deal with offensive, the Intrusions Detection Systems (IDSs) suffers from high false positive which leads to highly bad accuracy rate. So this work is suggested to implement (IDS) by using a Recursive Feature Elimination to select features and use Deep Neural Network (DNN) and Recurrent Neural Network (RNN) for classification, the suggested model gives good results with high accuracy rate reaching 94%, DNN was used in the binary classification to classify either attack or Normal, while RNN was used in the classifications for the five classes (Normal, Dos, Probe, R2L, U2R). The system was implemented by using (NSL-KDD) dataset, which was very efficient for offline analyses systems for IDS.                                                                                                   

Neural Networks for Intrusion Detection

2011 ◽  
pp. 2800-2805
Author(s):  
Rui Ma
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Processing Technique ◽  
Rapid Expansion ◽  
Uncertain Information ◽  
Active Defense ◽  
Modern Computer

With the rapid expansion of computer networks, network security has become a crucial issue for modern computer systems. As an important and active defense technology, the intrusion detection system (IDS) plays an important role in defensive systems. IDSs provide real-time protection from interior attacks, exterior attacks, and invalid operations, and it can intercept intrusions and respond whenever the network system integrity is violated (Ma, 2004). Many intrusion detection approaches have been deeply researched and some widely deployed. But the diversification, complexity, and scale of intrusions raise new demands for IDSs. Neural networks are tolerant of imprecise data and uncertain information. With their inherent ability to generalize from learned data they seem to be an appropriate approach to IDSs (Hofmann, Schmitz, & Sick, 2003). This article discusses the detection of distributed denial-of-service (DDoS) attacks using arti- ficial neural networks techniques. The implementation of a distributed intelligent intrusion detection system (DIIDS) is described, including both the data processing technique and neural networks approaches adopted.

scholarly journals HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 834
Author(s):  
Muhammad Ashfaq Khan
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Complex Nature ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

scholarly journals DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

2020 ◽  
Author(s):  
Sriram Srinivasan ◽  
Shashank A ◽  
vinayakumar R ◽  
Soman KP
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Intrusion Detection System ◽  
Input Data ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Traffic Data ◽  
Data Set ◽  
Research Problems

In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

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